A more efficient microgrid operation through the integration of probabilistic solar forecasts

Ramahatana, Faly; Salle, Josselin Le Gal La; Lauret, Philippe; David, Mathieu

doi:10.1016/j.segan.2022.100783

Cited by 5 publications

(3 citation statements)

References 56 publications

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“…Additionally, a day-ahead probabilistic PVF and wind power plant forecasting in Sweden was analyzed (Lindberg et al, 2023) considering the trading potential and immediate forecast feedback. Other recent studies investigated the probabilistic PVF (Pretto et al, 2022), (Ramahatana et al, 2022), (Mayer & Yang, 2023). In (Ramahatana et al, 2022), for instance, the authors proposed a more efficient operation of a microgrid using a probabilistic PVF, whereas in (Mayer & Yang, 2023), the authors paired the ensemble numerical weather prediction and ensemble physical model chain in order to obtain a better probabilistic PVF.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Other recent studies investigated the probabilistic PVF (Pretto et al, 2022), (Ramahatana et al, 2022), (Mayer & Yang, 2023). In (Ramahatana et al, 2022), for instance, the authors proposed a more efficient operation of a microgrid using a probabilistic PVF, whereas in (Mayer & Yang, 2023), the authors paired the ensemble numerical weather prediction and ensemble physical model chain in order to obtain a better probabilistic PVF. Optimal operation and the size of the storage facilities based on a probabilistic PVF were investigated in (Besson et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

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Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

Bâra,

Oprea

2024

Journal of Forecasting

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The creation of features makes the difference in improving the photovoltaic forecast (PVF) for on‐grid, hybrid and off‐grid PV systems. The importance of the PVF is tremendous, and it can be essential in optimizing the home appliances to maximize the Renewable Energy Sources (RES) usage or to create performant bids for the electricity market. Several use cases are considered from the connectivity point of view. Therefore, in this paper, we propose a Weather Prediction Error (WPE)‐based method that uses a Stacking Regressor (SR) for various PV systems that coexist in the emerging Energy Communities (EC) landscape. The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE. The forecast results of four types of PV systems from size and connectivity point of view are investigated. Compared with individual Machine Learning (ML) models, R2 increases with more than 3% with the SR and with more than 6% after applying the adjustment coefficients. Nevertheless, the major improvement is recorded for the off‐grid inverter and for the large industrial PV power plant, demonstrating that the proposed model is suitable for these types of systems. The other metrics improved as well, especially Mean Average Error (MAE) that decreases between 10% and 23%. A significant decrease is in the case of the industrial on‐grid PV, from 130 kW to 123 kW using the SR and to 107 kW after adjustments. This represents around 18% from the initial MAE. The ratio of daily deviations is also improved using the SR. For all the PV systems, the ratio stabilizes in a shorter interval, from daily values between 0.78 and 1.33 obtained with the ML models to values between 0.83 and 1.24 obtained with the SR. After the final adjustments, the interval becomes shorter, having daily values between 0.90 and 1.10.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

Bâra,

Oprea

2024

Journal of Forecasting

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“…The main issue is not to install new PV capacity but to achieve a techno-economic optimum to reduce the operation cost of the microgrid. Complementary approaches have already been tested to reach this goal: a combination of PV with compressed air energy storage (Castaing-Lasvignottes et al, 2016;Simpore et al, 2019) and a predictive Energy Management System (EMS) fed by probabilistic solar forecasts and load forecasts (Ramahatana et al, 2022). But these works were restricted to the Enerpos building.…”

Section: A Grid-connected Microgridmentioning

confidence: 99%

A Set of Study Cases for the Massive Integration of Solar Renewables in Non-Interconnected Areas

David,

Nahary Andriamandroso,

Behrensdorff Poulsen

et al. 2023

Proceedings of Solar World Congress 2023

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Integrating Bayesian inference and neural ODEs for microgrids dynamics parameters estimation

Fadoul,

Çağlar

2024

Sustainable Energy, Grids and Networks

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A more efficient microgrid operation through the integration of probabilistic solar forecasts

Cited by 5 publications

References 56 publications

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

A Set of Study Cases for the Massive Integration of Solar Renewables in Non-Interconnected Areas

Integrating Bayesian inference and neural ODEs for microgrids dynamics parameters estimation

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